Until now, there are two main challenges leading to performance bottlenecks in AAI. Firstly, most of existing works only used the audio speech feature to predict the articulatory movement without exploiting any additional features (i.e., lip feature, speaker identity feature or the content feature). Secondly, some works devoted to improve the generalization performance for the speaker-independent AAI, but their methods either lost the personalized information [1]}, needed large
amounts of data [2]} or required additional phoneme information [3]}.
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